Background

This file is designed to use CDC data to assess coronavirus disease burden by state, including creating and analyzing state-level clusters.

Through March 7, 2021, The COVID Tracking Project collected and integrated data on tests, cases, hospitalizations, deaths, and the like by state and date. The latest code for using this data is available in Coronavirus_Statistics_CTP_v004.Rmd.

The COVID Tracking Project suggest that US federal data sources are now sufficiently robust to be used for analyses that previously relied on COVID Tracking Project. This code is an attempt to update modules in Coronavirus_Statistics_CTP_v004.Rmd to leverage US federal data.

The code in this module builds on code available in _v004, with function and mapping files updated:

Broadly, the CDC data analyzed by this module includes:

Functions and Mapping Files

The tidyverse package is loaded and functions are sourced:

# The tidyverse functions are routinely used without package::function format
library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.8     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(geofacet)

# Functions are available in source file
source("./Generic_Added_Utility_Functions_202105_v001.R")
source("./Coronavirus_CDC_Daily_Functions_v002.R")

A series of mapping files are also available to allow for parameterized processing. Mappings include:

These default parameters are maintained in a separate .R file and can be sourced:

source("./Coronavirus_CDC_Daily_Default_Mappings_v002.R")

Example Code Processing

The function is run to download and process the latest CDC case, hospitalization, and death data:

readList <- list("cdcDaily"="./RInputFiles/Coronavirus/CDC_dc_downloaded_220907.csv", 
                 "cdcHosp"="./RInputFiles/Coronavirus/CDC_h_downloaded_220907.csv", 
                 "vax"="./RInputFiles/Coronavirus/vaxData_downloaded_220907.csv"
                 )
compareList <- list("cdcDaily"=readFromRDS("cdc_daily_220805")$dfRaw$cdcDaily, 
                    "cdcHosp"=readFromRDS("cdc_daily_220805")$dfRaw$cdcHosp, 
                    "vax"=readFromRDS("cdc_daily_220805")$dfRaw$vax
                    )

cdc_daily_220907 <- readRunCDCDaily(thruLabel="Sep 05, 2022", 
                                    downloadTo=lapply(readList, FUN=function(x) if(file.exists(x)) NA else x), 
                                    readFrom=readList,
                                    compareFile=compareList, 
                                    writeLog=NULL, 
                                    useClusters=readFromRDS("cdc_daily_210528")$useClusters, 
                                    weightedMeanAggs=c("tcpm7", "tdpm7", "cpm7", "dpm7", "hpm7", 
                                                       "vxcpm7", "vxcgte65pct"
                                                       ),
                                    skipAssessmentPlots=FALSE, 
                                    brewPalette="Paired"
                                    )
## Rows: 57480 Columns: 15
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): submission_date, state, created_at, consent_cases, consent_deaths
## dbl (10): tot_cases, conf_cases, prob_cases, new_case, pnew_case, tot_death,...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##          date       name newValue refValue absDelta   pctDelta
## 1  2022-07-31 new_deaths      116       23       93 1.33812950
## 2  2022-07-30 new_deaths      130       34       96 1.17073171
## 3  2022-07-23 new_deaths      158      109       49 0.36704120
## 4  2022-07-24 new_deaths      170      126       44 0.29729730
## 5  2022-08-01 new_deaths      433      347       86 0.22051282
## 6  2022-07-28 new_deaths      518      434       84 0.17647059
## 7  2022-07-16 new_deaths      151      127       24 0.17266187
## 8  2022-07-29 new_deaths      639      543       96 0.16243655
## 9  2022-07-25 new_deaths      306      265       41 0.14360771
## 10 2022-08-03 new_deaths      716      632       84 0.12462908
## 11 2022-08-02 new_deaths      715      632       83 0.12323682
## 12 2022-07-27 new_deaths      703      634       69 0.10321616
## 13 2022-07-10 new_deaths      114      103       11 0.10138249
## 14 2022-07-22 new_deaths      628      580       48 0.07947020
## 15 2022-06-18 new_deaths      105       97        8 0.07920792
## 16 2022-07-26 new_deaths      643      596       47 0.07586764
## 17 2022-07-04 new_deaths      138      128       10 0.07518797
## 18 2022-07-18 new_deaths      361      337       24 0.06876791
## 19 2022-07-21 new_deaths      500      471       29 0.05973223
## 20 2022-07-09 new_deaths      110      104        6 0.05607477
## 21 2022-07-30  new_cases    38338    32823     5515 0.15500063
## 22 2022-07-31  new_cases    39803    35276     4527 0.12059298
## 23 2022-08-01  new_cases   126242   133013     6771 0.05223429

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     NC tot_deaths 11352019 11325521    26498 0.002336938
## 2     KY tot_deaths  6954858  6939424    15434 0.002221633
## 3     NC new_deaths    26101    25692      409 0.015793640
## 4     KY new_deaths    16647    16438      209 0.012634124
## 5     FL new_deaths    78609    77823      786 0.010049095
## 6     AL new_deaths    20081    19974      107 0.005342654
## 7     SC new_deaths    18211    18192       19 0.001043870
## 8     SC  new_cases  1626423  1605165    21258 0.013156380
## 9     KY  new_cases  1489715  1479668    10047 0.006767062
## 10    NC  new_cases  3026839  3022204     4635 0.001532474
## 
## 
## 
## Raw file for cdcDaily:
## Rows: 57,480
## Columns: 15
## $ date           <date> 2021-03-11, 2021-12-01, 2022-01-02, 2021-09-01, 2021-0…
## $ state          <chr> "KS", "ND", "AS", "ND", "IN", "FL", "TN", "PR", "PW", "…
## $ tot_cases      <dbl> 297229, 163565, 11, 118491, 668765, 3510205, 64885, 173…
## $ conf_cases     <dbl> 241035, 135705, NA, 107475, NA, NA, 64371, 144788, NA, …
## $ prob_cases     <dbl> 56194, 27860, NA, 11016, NA, NA, 514, 29179, NA, NA, NA…
## $ new_cases      <dbl> 0, 589, 0, 536, 487, 9979, 1816, 667, 0, 317, 0, 28, 8,…
## $ pnew_case      <dbl> 0, 220, 0, 66, 0, 2709, 30, 274, 0, 0, 0, 5, 0, 46, 70,…
## $ tot_deaths     <dbl> 4851, 1907, 0, 1562, 12710, 56036, 749, 2911, 0, 561, 0…
## $ conf_death     <dbl> NA, NA, NA, NA, 12315, NA, 722, 2482, NA, NA, NA, 1601,…
## $ prob_death     <dbl> NA, NA, NA, NA, 395, NA, 27, 429, NA, NA, NA, 366, NA, …
## $ new_deaths     <dbl> 0, 9, 0, 1, 7, 294, 8, 8, 0, 12, 0, 0, 0, 5, 0, 4, 0, 0…
## $ pnew_death     <dbl> 0, 0, 0, 0, 2, 26, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ created_at     <chr> "03/12/2021 03:20:13 PM", "12/02/2021 02:35:20 PM", "01…
## $ consent_cases  <chr> "Agree", "Agree", NA, "Agree", "Not agree", "Not agree"…
## $ consent_deaths <chr> "N/A", "Not agree", NA, "Not agree", "Agree", "Not agre…
## Rows: 49367 Columns: 135
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr    (1): state
## dbl  (132): critical_staffing_shortage_today_yes, critical_staffing_shortage...
## lgl    (1): geocoded_state
## date   (1): date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: 
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 33
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 5 and at least 5%
## 
##         date     name newValue refValue absDelta  pctDelta
## 1 2020-07-25 hosp_ped     3964     4594      630 0.1472307

## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
##    state       name newValue refValue absDelta    pctDelta
## 1     ND        inp   122358   122070      288 0.002356522
## 2     NH   hosp_ped     1127     1167       40 0.034873583
## 3     KS   hosp_ped     4891     4725      166 0.034525790
## 4     ME   hosp_ped     2387     2338       49 0.020740741
## 5     KY   hosp_ped    20285    20665      380 0.018559219
## 6     WV   hosp_ped     5686     5753       67 0.011714311
## 7     VA   hosp_ped    18388    18192      196 0.010716238
## 8     TN   hosp_ped    22215    22423      208 0.009319414
## 9     NM   hosp_ped     8054     8114       60 0.007422068
## 10    SC   hosp_ped     9035     9092       57 0.006288961
## 11    DE   hosp_ped     5277     5310       33 0.006234061
## 12    NJ   hosp_ped    19499    19618      119 0.006084311
## 13    UT   hosp_ped    10271    10210       61 0.005956740
## 14    MS   hosp_ped    11803    11854       51 0.004311620
## 15    AL   hosp_ped    20947    21025       78 0.003716764
## 16    VT   hosp_ped      540      542        2 0.003696858
## 17    WY   hosp_ped      859      856        3 0.003498542
## 18    MA   hosp_ped    12619    12657       38 0.003006805
## 19    NC   hosp_ped    30541    30453       88 0.002885530
## 20    PR   hosp_ped    23021    22959       62 0.002696825
## 21    IL   hosp_ped    44084    44202      118 0.002673131
## 22    AK   hosp_ped     2664     2657        7 0.002631084
## 23    MO   hosp_ped    39841    39939       98 0.002456756
## 24    PA   hosp_ped    55078    55211      133 0.002411845
## 25    AR   hosp_ped    12767    12747       20 0.001567767
## 26    CO   hosp_ped    22421    22387       34 0.001517586
## 27    OH   hosp_ped    91382    91261      121 0.001324989
## 28    AZ   hosp_ped    27563    27532       31 0.001125329
## 29    MD   hosp_ped    17240    17221       19 0.001102696
## 30    ND hosp_adult   115920   115630      290 0.002504859
## 
## 
## 
## Raw file for cdcHosp:
## Rows: 49,367
## Columns: 135
## $ state                                                                        <chr> …
## $ date                                                                         <date> …
## $ critical_staffing_shortage_today_yes                                         <dbl> …
## $ critical_staffing_shortage_today_no                                          <dbl> …
## $ critical_staffing_shortage_today_not_reported                                <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_yes                       <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_no                        <dbl> …
## $ critical_staffing_shortage_anticipated_within_week_not_reported              <dbl> …
## $ hospital_onset_covid                                                         <dbl> …
## $ hospital_onset_covid_coverage                                                <dbl> …
## $ inpatient_beds                                                               <dbl> …
## $ inpatient_beds_coverage                                                      <dbl> …
## $ inpatient_beds_used                                                          <dbl> …
## $ inpatient_beds_used_coverage                                                 <dbl> …
## $ inp                                                                          <dbl> …
## $ inpatient_beds_used_covid_coverage                                           <dbl> …
## $ previous_day_admission_adult_covid_confirmed                                 <dbl> …
## $ previous_day_admission_adult_covid_confirmed_coverage                        <dbl> …
## $ previous_day_admission_adult_covid_suspected                                 <dbl> …
## $ previous_day_admission_adult_covid_suspected_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_coverage                    <dbl> …
## $ previous_day_admission_pediatric_covid_suspected                             <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_coverage                    <dbl> …
## $ staffed_adult_icu_bed_occupancy                                              <dbl> …
## $ staffed_adult_icu_bed_occupancy_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid                                   <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_coverage                          <dbl> …
## $ hosp_adult                                                                   <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid                            <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_coverage                   <dbl> …
## $ hosp_ped                                                                     <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_coverage               <dbl> …
## $ total_staffed_adult_icu_beds                                                 <dbl> …
## $ total_staffed_adult_icu_beds_coverage                                        <dbl> …
## $ inpatient_beds_utilization                                                   <dbl> …
## $ inpatient_beds_utilization_coverage                                          <dbl> …
## $ inpatient_beds_utilization_numerator                                         <dbl> …
## $ inpatient_beds_utilization_denominator                                       <dbl> …
## $ percent_of_inpatients_with_covid                                             <dbl> …
## $ percent_of_inpatients_with_covid_coverage                                    <dbl> …
## $ percent_of_inpatients_with_covid_numerator                                   <dbl> …
## $ percent_of_inpatients_with_covid_denominator                                 <dbl> …
## $ inpatient_bed_covid_utilization                                              <dbl> …
## $ inpatient_bed_covid_utilization_coverage                                     <dbl> …
## $ inpatient_bed_covid_utilization_numerator                                    <dbl> …
## $ inpatient_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_covid_utilization                                              <dbl> …
## $ adult_icu_bed_covid_utilization_coverage                                     <dbl> …
## $ adult_icu_bed_covid_utilization_numerator                                    <dbl> …
## $ adult_icu_bed_covid_utilization_denominator                                  <dbl> …
## $ adult_icu_bed_utilization                                                    <dbl> …
## $ adult_icu_bed_utilization_coverage                                           <dbl> …
## $ adult_icu_bed_utilization_numerator                                          <dbl> …
## $ adult_icu_bed_utilization_denominator                                        <dbl> …
## $ geocoded_state                                                               <lgl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_coverage                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79`                         <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_coverage`                <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+`                           <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_coverage`                  <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown                         <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_coverage                <dbl> …
## $ deaths_covid                                                                 <dbl> …
## $ deaths_covid_coverage                                                        <dbl> …
## $ on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses                   <dbl> …
## $ on_hand_supply_therapeutic_b_bamlanivimab_courses                            <dbl> …
## $ on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses                 <dbl> …
## $ previous_week_therapeutic_a_casirivimab_imdevimab_courses_used               <dbl> …
## $ previous_week_therapeutic_b_bamlanivimab_courses_used                        <dbl> …
## $ previous_week_therapeutic_c_bamlanivimab_etesevimab_courses_used             <dbl> …
## $ icu_patients_confirmed_influenza                                             <dbl> …
## $ icu_patients_confirmed_influenza_coverage                                    <dbl> …
## $ previous_day_admission_influenza_confirmed                                   <dbl> …
## $ previous_day_admission_influenza_confirmed_coverage                          <dbl> …
## $ previous_day_deaths_covid_and_influenza                                      <dbl> …
## $ previous_day_deaths_covid_and_influenza_coverage                             <dbl> …
## $ previous_day_deaths_influenza                                                <dbl> …
## $ previous_day_deaths_influenza_coverage                                       <dbl> …
## $ total_patients_hospitalized_confirmed_influenza                              <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_coverage           <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_coverage                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied                                         <dbl> …
## $ all_pediatric_inpatient_bed_occupied_coverage                                <dbl> …
## $ all_pediatric_inpatient_beds                                                 <dbl> …
## $ all_pediatric_inpatient_beds_coverage                                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4                         <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_coverage                <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17                       <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_coverage              <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_coverage               <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_coverage            <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid                               <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_coverage                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy                                          <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_coverage                                 <dbl> …
## $ total_staffed_pediatric_icu_beds                                             <dbl> …
## $ total_staffed_pediatric_icu_beds_coverage                                    <dbl> …
## Rows: 36184 Columns: 96
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (2): Date, Location
## dbl (94): MMWR_week, Distributed, Distributed_Janssen, Distributed_Moderna, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

## 
## *** File has been checked for uniqueness by: state date

## 
## 
## Checking for similarity of: column names
## In reference but not in current: 
## In current but not in reference: Distributed_Novavax Administered_Novavax Series_Complete_Novavax
## 
## Checking for similarity of: date
## In reference but not in current: 0
## In current but not in reference: 4
## 
## Checking for similarity of: state
## In reference but not in current: 
## In current but not in reference:

## 
## 
## ***Differences of at least 1 and at least 1%
## 
## [1] date     name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## ***Differences of at least 0 and at least 0.1%
## 
## [1] state    name     newValue refValue absDelta pctDelta
## <0 rows> (or 0-length row.names)
## 
## 
## 
## Raw file for vax:
## Rows: 36,184
## Columns: 96
## $ date                                   <date> 2022-08-31, 2022-08-31, 2022-0…
## $ MMWR_week                              <dbl> 35, 35, 35, 35, 35, 35, 35, 35,…
## $ state                                  <chr> "PW", "SD", "MA", "HI", "RI", "…
## $ Distributed                            <dbl> 47090, 2141765, 18793570, 38391…
## $ Distributed_Janssen                    <dbl> 3800, 92800, 626200, 124700, 90…
## $ Distributed_Moderna                    <dbl> 30000, 847500, 7168380, 1461820…
## $ Distributed_Pfizer                     <dbl> 13290, 1199665, 10993590, 22498…
## $ Distributed_Novavax                    <dbl> 0, 1800, 5400, 2800, 3200, 200,…
## $ Distributed_Unk_Manuf                  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Dist_Per_100K                          <dbl> 218698, 242101, 272667, 271153,…
## $ Distributed_Per_100k_5Plus             <dbl> 231139, 260083, 287577, 288518,…
## $ Distributed_Per_100k_12Plus            <dbl> 252561, 290172, 312354, 316997,…
## $ Distributed_Per_100k_18Plus            <dbl> 283966, 320836, 339252, 344011,…
## $ Distributed_Per_100k_65Plus            <dbl> 2363960, 1410250, 1607210, 1430…
## $ vxa                                    <dbl> 49416, 1511407, 15773792, 31561…
## $ Administered_5Plus                     <dbl> 49373, 1507671, 15687231, 31448…
## $ Administered_12Plus                    <dbl> 46683, 1451767, 15028043, 30184…
## $ Administered_18Plus                    <dbl> 43018, 1359766, 14038745, 28178…
## $ Administered_65Plus                    <dbl> 5346, 453691, 3769576, 842523, …
## $ Administered_Janssen                   <dbl> 2357, 42334, 407539, 71355, 664…
## $ Administered_Moderna                   <dbl> 37794, 586034, 6193704, 1157104…
## $ Administered_Pfizer                    <dbl> 9098, 882891, 9171774, 1927032,…
## $ Administered_Novavax                   <dbl> 0, 0, 295, 10, 219, 1, 45, 25, …
## $ Administered_Unk_Manuf                 <dbl> 167, 148, 480, 697, 2259, 9, 25…
## $ Admin_Per_100k                         <dbl> 229500, 170846, 228854, 222915,…
## $ Admin_Per_100k_5Plus                   <dbl> 242345, 183083, 240044, 236338,…
## $ Admin_Per_100k_12Plus                  <dbl> 250378, 196689, 249770, 249232,…
## $ Admin_Per_100k_18Plus                  <dbl> 259410, 203693, 253421, 252497,…
## $ Admin_Per_100k_65Plus                  <dbl> 268373, 298734, 322370, 313850,…
## $ Recip_Administered                     <dbl> 49797, 1533994, 15858274, 31873…
## $ Administered_Dose1_Recip               <dbl> 20575, 700878, 6982383, 1266721…
## $ Administered_Dose1_Pop_Pct             <dbl> 95.0, 79.2, 95.0, 89.5, 95.0, 8…
## $ Administered_Dose1_Recip_5Plus         <dbl> 20547, 698199, 6928829, 1259039…
## $ Administered_Dose1_Recip_5PlusPop_Pct  <dbl> 95.0, 84.8, 95.0, 94.6, 95.0, 9…
## $ Administered_Dose1_Recip_12Plus        <dbl> 19119, 668658, 6593289, 1196906…
## $ Administered_Dose1_Recip_12PlusPop_Pct <dbl> 95.0, 90.6, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_18Plus        <dbl> 17584, 622099, 6127404, 1107067…
## $ Administered_Dose1_Recip_18PlusPop_Pct <dbl> 95.0, 93.2, 95.0, 95.0, 95.0, 9…
## $ Administered_Dose1_Recip_65Plus        <dbl> 1876, 182192, 1462735, 275645, …
## $ Administered_Dose1_Recip_65PlusPop_Pct <dbl> 94.2, 95.0, 95.0, 95.0, 95.0, 8…
## $ vxc                                    <dbl> 18338, 563276, 5570460, 1128707…
## $ vxcpoppct                              <dbl> 85.2, 63.7, 80.8, 79.7, 84.9, 8…
## $ Series_Complete_5Plus                  <dbl> 18330, 563050, 5551263, 1126786…
## $ Series_Complete_5PlusPop_Pct           <dbl> 90.0, 68.4, 84.9, 84.7, 89.4, 9…
## $ Series_Complete_12Plus                 <dbl> 17241, 539812, 5278994, 1071938…
## $ Series_Complete_12PlusPop_Pct          <dbl> 92.5, 73.1, 87.7, 88.5, 92.3, 9…
## $ vxcgte18                               <dbl> 15791, 503622, 4896487, 990569,…
## $ vxcgte18pct                            <dbl> 95.0, 75.4, 88.4, 88.8, 93.0, 9…
## $ vxcgte65                               <dbl> 1811, 151094, 1165453, 252765, …
## $ vxcgte65pct                            <dbl> 90.9, 95.0, 95.0, 94.2, 95.0, 8…
## $ Series_Complete_Janssen                <dbl> 2361, 39918, 384642, 66056, 611…
## $ Series_Complete_Moderna                <dbl> 12724, 204498, 1968460, 371324,…
## $ Series_Complete_Pfizer                 <dbl> 3164, 318781, 3216940, 691089, …
## $ Series_Complete_Novavax                <dbl> 0, 2, 38, 1, 52, 1, 6, 6, 38, 2…
## $ Series_Complete_Unk_Manuf              <dbl> 82, 70, 290, 215, 602, 3, 591, …
## $ Series_Complete_Janssen_5Plus          <dbl> 2361, 39914, 384637, 66028, 611…
## $ Series_Complete_Moderna_5Plus          <dbl> 12724, 204289, 1955713, 370535,…
## $ Series_Complete_Pfizer_5Plus           <dbl> 3163, 318775, 3210586, 690007, …
## $ Series_Complete_Unk_Manuf_5Plus        <dbl> 82, 70, 289, 215, 587, 3, 591, …
## $ Series_Complete_Janssen_12Plus         <dbl> 2361, 39912, 384612, 66026, 611…
## $ Series_Complete_Moderna_12Plus         <dbl> 12724, 204257, 1953836, 370413,…
## $ Series_Complete_Pfizer_12Plus          <dbl> 2074, 295572, 2940222, 635307, …
## $ Series_Complete_Unk_Manuf_12Plus       <dbl> 82, 69, 286, 191, 572, 3, 588, …
## $ Series_Complete_Janssen_18Plus         <dbl> 2361, 39882, 383306, 65839, 611…
## $ Series_Complete_Moderna_18Plus         <dbl> 12723, 204149, 1948279, 369555,…
## $ Series_Complete_Pfizer_18Plus          <dbl> 625, 259525, 2564603, 555015, 4…
## $ Series_Complete_Unk_Manuf_18Plus       <dbl> 82, 64, 262, 159, 543, 3, 574, …
## $ Series_Complete_Janssen_65Plus         <dbl> 227, 5079, 74665, 11821, 6832, …
## $ Series_Complete_Moderna_65Plus         <dbl> 1542, 74263, 531381, 111196, 86…
## $ Series_Complete_Pfizer_65Plus          <dbl> 40, 71727, 559321, 129727, 1005…
## $ Series_Complete_Unk_Manuf_65Plus       <dbl> 2, 25, 80, 21, 162, 0, 263, 69,…
## $ Additional_Doses                       <dbl> 12048, 248903, 2987198, 646528,…
## $ Additional_Doses_Vax_Pct               <dbl> 65.7, 44.2, 53.6, 57.3, 56.1, 5…
## $ Additional_Doses_5Plus                 <dbl> 12048, 248900, 2987162, 646515,…
## $ Additional_Doses_5Plus_Vax_Pct         <dbl> 65.7, 44.2, 53.8, 57.4, 56.2, 5…
## $ Additional_Doses_12Plus                <dbl> 11872, 246180, 2938665, 637193,…
## $ Additional_Doses_12Plus_Vax_Pct        <dbl> 68.9, 45.6, 55.7, 59.4, 58.4, 5…
## $ Additional_Doses_18Plus                <dbl> 11181, 236970, 2791202, 606621,…
## $ Additional_Doses_18Plus_Vax_Pct        <dbl> 70.8, 47.1, 57.0, 61.2, 60.1, 5…
## $ Additional_Doses_50Plus                <dbl> 4815, 163923, 1630617, 376685, …
## $ Additional_Doses_50Plus_Vax_Pct        <dbl> 80.1, 58.2, 66.0, 75.0, 71.4, 7…
## $ Additional_Doses_65Plus                <dbl> 1575, 98849, 840257, 208155, 15…
## $ Additional_Doses_65Plus_Vax_Pct        <dbl> 87.0, 65.4, 72.1, 82.4, 79.0, 7…
## $ Additional_Doses_Moderna               <dbl> 10870, 109000, 1349812, 272149,…
## $ Additional_Doses_Pfizer                <dbl> 1176, 136782, 1609614, 367759, …
## $ Additional_Doses_Janssen               <dbl> 2, 3093, 27721, 6500, 5296, 217…
## $ Additional_Doses_Unk_Manuf             <dbl> 0, 26, 45, 118, 129, 0, 438, 78…
## $ Second_Booster                         <dbl> NA, NA, NA, NA, NA, NA, NA, NA,…
## $ Second_Booster_50Plus                  <dbl> 1126, 53725, 590595, 170399, 10…
## $ Second_Booster_50Plus_Vax_Pct          <dbl> 23.4, 32.8, 36.2, 45.2, 34.4, 1…
## $ Second_Booster_65Plus                  <dbl> 383, 39382, 379347, 111314, 667…
## $ Second_Booster_65Plus_Vax_Pct          <dbl> 24.3, 39.8, 45.1, 53.5, 43.5, 2…
## $ Second_Booster_Janssen                 <dbl> 0, 27, 253, 120, 151, 1, 119, 2…
## $ Second_Booster_Moderna                 <dbl> 1148, 24919, 309097, 87335, 498…
## $ Second_Booster_Pfizer                  <dbl> 22, 30731, 314869, 91565, 57827…
## $ Second_Booster_Unk_Manuf               <dbl> 0, 2, 10, 15, 53, 0, 80, 28, 21…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 6
##   isType tot_cases tot_deaths     new_cases    new_deaths         n
##   <chr>      <dbl>      <dbl>         <dbl>         <dbl>     <dbl>
## 1 before  3.53e+10    5.16e+8 93993694      1025546       56522    
## 2 after   3.51e+10    5.14e+8 92929415      1019927       48858    
## 3 pctchg  6.80e- 3    4.57e-3        0.0113       0.00548     0.136
## 
## 
## Processed for cdcDaily:
## Rows: 48,858
## Columns: 6
## $ date       <date> 2021-03-11, 2021-12-01, 2021-09-01, 2021-03-08, 2021-09-17…
## $ state      <chr> "KS", "ND", "ND", "IN", "FL", "TN", "IA", "SD", "HI", "MA",…
## $ tot_cases  <dbl> 297229, 163565, 118491, 668765, 3510205, 64885, 20015, 1226…
## $ tot_deaths <dbl> 4851, 1907, 1562, 12710, 56036, 749, 561, 1967, 17, 17818, …
## $ new_cases  <dbl> 0, 589, 536, 487, 9979, 1816, 317, 28, 8, 451, 1040, 133, 0…
## $ new_deaths <dbl> 0, 9, 1, 7, 294, 8, 12, 0, 0, 5, 4, 0, 0, 5, 1, 3, 0, 0, 22…
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 5
##   isType     inp hosp_adult     hosp_ped          n
##   <chr>    <dbl>      <dbl>        <dbl>      <dbl>
## 1 before 5.11e+7    4.45e+7 1229807      49367     
## 2 after  5.09e+7    4.43e+7 1205197      47181     
## 3 pctchg 5.37e-3    5.13e-3       0.0200     0.0443
## 
## 
## Processed for cdcHosp:
## Rows: 47,181
## Columns: 5
## $ date       <date> 2021-01-06, 2021-01-06, 2020-12-31, 2020-12-30, 2020-12-29…
## $ state      <chr> "MA", "OR", "SD", "RI", "OR", "OH", "LA", "WV", "VT", "WY",…
## $ inp        <dbl> 2232, 583, 282, 471, 626, 5534, 1461, 242, 0, 71, 1, 91, 49…
## $ hosp_adult <dbl> 2209, 568, 280, 469, 615, 5443, 1449, 241, 0, 70, 1, 90, 48…
## $ hosp_ped   <dbl> 23, 15, 2, 2, 11, 91, 12, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, …
## 
## Column sums before and after applying filtering rules:
## # A tibble: 3 × 9
##   isType      vxa      vxc   vxcpoppct vxcgte65 vxcgt…¹ vxcgte18 vxcgt…²       n
##   <chr>     <dbl>    <dbl>       <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
## 1 before 4.14e+11 1.70e+11 1511640.    4.31e+10 2.24e+6 1.57e+11 1.78e+6 3.62e+4
## 2 after  2.00e+11 8.22e+10 1265373.    2.09e+10 1.98e+6 7.61e+10 1.51e+6 2.86e+4
## 3 pctchg 5.18e- 1 5.16e- 1       0.163 5.16e- 1 1.14e-1 5.16e- 1 1.54e-1 2.09e-1
## # … with abbreviated variable names ¹​vxcgte65pct, ²​vxcgte18pct
## 
## 
## Processed for vax:
## Rows: 28,611
## Columns: 9
## $ date        <date> 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-31, 2022-08-3…
## $ state       <chr> "SD", "MA", "HI", "RI", "MT", "WY", "LA", "KS", "IN", "MS"…
## $ vxa         <dbl> 1511407, 15773792, 3156198, 2353711, 1675440, 778457, 6536…
## $ vxc         <dbl> 563276, 5570460, 1128707, 899544, 618143, 300240, 2526855,…
## $ vxcpoppct   <dbl> 63.7, 80.8, 79.7, 84.9, 57.8, 51.9, 54.4, 63.3, 56.8, 53.0…
## $ vxcgte65    <dbl> 151094, 1165453, 252765, 194268, 180238, 84688, 642150, 44…
## $ vxcgte65pct <dbl> 95.0, 95.0, 94.2, 95.0, 87.3, 85.4, 86.7, 94.6, 88.5, 85.2…
## $ vxcgte18    <dbl> 503622, 4896487, 990569, 794734, 562722, 275699, 2323648, …
## $ vxcgte18pct <dbl> 75.4, 88.4, 88.8, 93.0, 67.0, 62.0, 65.2, 74.2, 67.0, 63.3…
## 
## Integrated per capita data file:
## Rows: 49,071
## Columns: 34
## $ date        <date> 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-01, 2020-01-0…
## $ state       <chr> "AL", "HI", "IN", "LA", "MN", "MT", "NC", "TX", "AL", "HI"…
## $ tot_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tot_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_cases   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ new_deaths  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ inp         <dbl> NA, 0, 0, NA, 0, 0, 0, 0, NA, 0, 0, NA, 0, 0, 0, 1877, 0, …
## $ hosp_adult  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hosp_ped    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxa         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxc         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpoppct   <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte65pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcgte18pct <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm         <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm         <dbl> NA, 0.0000, 0.0000, NA, 0.0000, 0.0000, 0.0000, 0.0000, NA…
## $ ahpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tcpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ tdpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ cpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ dpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ hpm7        <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ ahpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ phpm7       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxapm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ vxcpm7      <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum unknown in Proj4 definition

saveToRDS(cdc_daily_220907, ovrWriteError=FALSE)

The function is run to download and process the latest hospitalization data:

# Run for latest data, save as RDS
indivHosp_20220907 <- downloadReadHospitalData(loc="./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv")
## 
## File ./RInputFiles/Coronavirus/HHS_Hospital_20220907.csv already exists
## File will not be downloaded since ovrWrite is not TRUE
## Rows: 269456 Columns: 128
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (11): hospital_pk, state, ccn, hospital_name, address, city, zip, hosp...
## dbl  (114): total_beds_7_day_avg, all_adult_hospital_beds_7_day_avg, all_adu...
## lgl    (2): is_metro_micro, is_corrected
## date   (1): collection_week
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 269,456
## Columns: 128
## $ hospital_pk                                                                        <chr> …
## $ collection_week                                                                    <date> …
## $ state                                                                              <chr> …
## $ ccn                                                                                <chr> …
## $ hospital_name                                                                      <chr> …
## $ address                                                                            <chr> …
## $ city                                                                               <chr> …
## $ zip                                                                                <chr> …
## $ hospital_subtype                                                                   <chr> …
## $ fips_code                                                                          <chr> …
## $ is_metro_micro                                                                     <lgl> …
## $ total_beds_7_day_avg                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_avg                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_avg                                        <dbl> …
## $ inpatient_beds_used_7_day_avg                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_avg                                <dbl> …
## $ inpatient_beds_used_covid_7_day_avg                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_avg                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_avg      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_avg                    <dbl> …
## $ inpatient_beds_7_day_avg                                                           <dbl> …
## $ total_icu_beds_7_day_avg                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_avg                                             <dbl> …
## $ icu_beds_used_7_day_avg                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_avg                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_avg                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_avg                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_avg                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_avg                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_avg                <dbl> …
## $ total_beds_7_day_sum                                                               <dbl> …
## $ all_adult_hospital_beds_7_day_sum                                                  <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_sum                                        <dbl> …
## $ inpatient_beds_used_7_day_sum                                                      <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_sum                                <dbl> …
## $ inpatient_beds_used_covid_7_day_sum                                                <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum          <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_sum                        <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_sum      <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_sum                    <dbl> …
## $ inpatient_beds_7_day_sum                                                           <dbl> …
## $ total_icu_beds_7_day_sum                                                           <dbl> …
## $ total_staffed_adult_icu_beds_7_day_sum                                             <dbl> …
## $ icu_beds_used_7_day_sum                                                            <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_sum                                          <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_sum                 <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_sum                               <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_sum                          <dbl> …
## $ icu_patients_confirmed_influenza_7_day_sum                                         <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_sum                <dbl> …
## $ total_beds_7_day_coverage                                                          <dbl> …
## $ all_adult_hospital_beds_7_day_coverage                                             <dbl> …
## $ all_adult_hospital_inpatient_beds_7_day_coverage                                   <dbl> …
## $ inpatient_beds_used_7_day_coverage                                                 <dbl> …
## $ all_adult_hospital_inpatient_bed_occupied_7_day_coverage                           <dbl> …
## $ inpatient_beds_used_covid_7_day_coverage                                           <dbl> …
## $ total_adult_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage     <dbl> …
## $ total_adult_patients_hospitalized_confirmed_covid_7_day_coverage                   <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_and_suspected_covid_7_day_coverage <dbl> …
## $ total_pediatric_patients_hospitalized_confirmed_covid_7_day_coverage               <dbl> …
## $ inpatient_beds_7_day_coverage                                                      <dbl> …
## $ total_icu_beds_7_day_coverage                                                      <dbl> …
## $ total_staffed_adult_icu_beds_7_day_coverage                                        <dbl> …
## $ icu_beds_used_7_day_coverage                                                       <dbl> …
## $ staffed_adult_icu_bed_occupancy_7_day_coverage                                     <dbl> …
## $ staffed_icu_adult_patients_confirmed_and_suspected_covid_7_day_coverage            <dbl> …
## $ staffed_icu_adult_patients_confirmed_covid_7_day_coverage                          <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_7_day_coverage                     <dbl> …
## $ icu_patients_confirmed_influenza_7_day_coverage                                    <dbl> …
## $ total_patients_hospitalized_confirmed_influenza_and_covid_7_day_coverage           <dbl> …
## $ previous_day_admission_adult_covid_confirmed_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_confirmed_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_confirmed_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_sum                         <dbl> …
## $ previous_day_covid_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_sum                             <dbl> …
## $ `previous_day_admission_adult_covid_suspected_18-19_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_20-29_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_30-39_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_40-49_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_50-59_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_60-69_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_70-79_7_day_sum`                     <dbl> …
## $ `previous_day_admission_adult_covid_suspected_80+_7_day_sum`                       <dbl> …
## $ previous_day_admission_adult_covid_suspected_unknown_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_sum                         <dbl> …
## $ previous_day_total_ED_visits_7_day_sum                                             <dbl> …
## $ previous_day_admission_influenza_confirmed_7_day_sum                               <dbl> …
## $ geocoded_hospital_address                                                          <chr> …
## $ hhs_ids                                                                            <chr> …
## $ previous_day_admission_adult_covid_confirmed_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_7_day_coverage                    <dbl> …
## $ previous_day_admission_adult_covid_suspected_7_day_coverage                        <dbl> …
## $ previous_day_admission_pediatric_covid_suspected_7_day_coverage                    <dbl> …
## $ previous_week_personnel_covid_vaccinated_doses_administered_7_day                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_none_7_day                                  <dbl> …
## $ total_personnel_covid_vaccinated_doses_one_7_day                                   <dbl> …
## $ total_personnel_covid_vaccinated_doses_all_7_day                                   <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_one_7_day                            <dbl> …
## $ previous_week_patients_covid_vaccinated_doses_all_7_day                            <dbl> …
## $ is_corrected                                                                       <lgl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_avg                                     <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_coverage                                <dbl> …
## $ all_pediatric_inpatient_bed_occupied_7_day_sum                                     <dbl> …
## $ all_pediatric_inpatient_beds_7_day_avg                                             <dbl> …
## $ all_pediatric_inpatient_beds_7_day_coverage                                        <dbl> …
## $ all_pediatric_inpatient_beds_7_day_sum                                             <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum                     <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum                   <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum                    <dbl> …
## $ previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum                 <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_avg                           <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage                      <dbl> …
## $ staffed_icu_pediatric_patients_confirmed_covid_7_day_sum                           <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_avg                                      <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_coverage                                 <dbl> …
## $ staffed_pediatric_icu_bed_occupancy_7_day_sum                                      <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_avg                                         <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_coverage                                    <dbl> …
## $ total_staffed_pediatric_icu_beds_7_day_sum                                         <dbl> …
## 
## Hospital Subtype Counts:
## # A tibble: 4 × 2
##   hospital_subtype               n
##   <chr>                      <int>
## 1 Childrens Hospitals         5077
## 2 Critical Access Hospitals  72331
## 3 Long Term                  18519
## 4 Short Term                173529
## 
## Records other than 50 states and DC
## # A tibble: 5 × 2
##   state     n
##   <chr> <int>
## 1 AS       54
## 2 GU      106
## 3 MP       46
## 4 PR     2864
## 5 VI      106
## 
## Record types for key metrics
## # A tibble: 10 × 5
##    name                                              `NA` Posit…¹ Value…²  Total
##    <chr>                                            <int>   <int>   <int>  <int>
##  1 all_adult_hospital_beds_7_day_avg                64869  204079     508 269456
##  2 all_adult_hospital_inpatient_bed_occupied_7_day…   143  247227   22086 269456
##  3 icu_beds_used_7_day_avg                             64  237310   32082 269456
##  4 inpatient_beds_7_day_avg                            67  268366    1023 269456
##  5 inpatient_beds_used_7_day_avg                       51  248042   21363 269456
##  6 inpatient_beds_used_covid_7_day_avg                 32  182000   87424 269456
##  7 staffed_icu_adult_patients_confirmed_and_suspec…   162  184312   84982 269456
##  8 total_adult_patients_hospitalized_confirmed_and…   121  181944   87391 269456
##  9 total_beds_7_day_avg                             63149  206009     298 269456
## 10 total_icu_beds_7_day_avg                            74  255402   13980 269456
## # … with abbreviated variable names ¹​Positive, ²​`Value -999999`
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

saveToRDS(indivHosp_20220907, ovrWriteError=FALSE)
## 
## File already exists: ./RInputFiles/Coronavirus/indivHosp_20220907.RDS 
## 
## Not replacing the existing file since ovrWrite=FALSE
## NULL

Post-processing is run, including hospital summaries:

# Create pivoted burden data
burdenPivotList_220907 <- postProcessCDCDaily(cdc_daily_220907, 
                                              dataThruLabel="Aug 2022", 
                                              keyDatesBurden=c("2022-08-31", "2022-02-28", 
                                                               "2021-08-31", "2021-02-28"
                                                               ),
                                              keyDatesVaccine=c("2022-08-31", "2022-03-31", 
                                                                "2021-10-31", "2021-05-31"
                                                                ), 
                                              returnData=TRUE
                                              )
## Joining, by = "state"
## 
## *** File has been checked for uniqueness by: state date name
## Warning: Removed 24 row(s) containing missing values (geom_path).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 24 rows containing missing values (position_stack).

## Warning: Removed 9 row(s) containing missing values (geom_path).

# Create hospitalized per capita data
hospPerCap_220907 <- hospAgePerCapita(readFromRDS("dfStateAgeBucket2019"), 
                                      lst=burdenPivotList_220907, 
                                      popVar="pop2019", 
                                      excludeState=c(), 
                                      cumStartDate="2020-07-15"
                                      )
## Warning: Removed 18 row(s) containing missing values (geom_path).

burdenPivotList_220907$hospAge %>%
    group_by(adultPed, confSusp, age, name) %>%
    summarize(value=sum(value, na.rm=TRUE), n=n(), .groups="drop")
## # A tibble: 18 × 6
##    adultPed confSusp  age   name                                     value     n
##    <chr>    <chr>     <chr> <chr>                                    <dbl> <int>
##  1 adult    confirmed 0-19  previous_day_admission_adult_covid_con… 4.70e4 49367
##  2 adult    confirmed 20-29 previous_day_admission_adult_covid_con… 2.86e5 49367
##  3 adult    confirmed 30-39 previous_day_admission_adult_covid_con… 4.12e5 49367
##  4 adult    confirmed 40-49 previous_day_admission_adult_covid_con… 4.96e5 49367
##  5 adult    confirmed 50-59 previous_day_admission_adult_covid_con… 7.91e5 49367
##  6 adult    confirmed 60-69 previous_day_admission_adult_covid_con… 1.04e6 49367
##  7 adult    confirmed 70-79 previous_day_admission_adult_covid_con… 1.04e6 49367
##  8 adult    confirmed 80+   previous_day_admission_adult_covid_con… 9.32e5 49367
##  9 adult    suspected 0-19  previous_day_admission_adult_covid_sus… 3.83e4 49367
## 10 adult    suspected 20-29 previous_day_admission_adult_covid_sus… 2.56e5 49367
## 11 adult    suspected 30-39 previous_day_admission_adult_covid_sus… 3.35e5 49367
## 12 adult    suspected 40-49 previous_day_admission_adult_covid_sus… 3.39e5 49367
## 13 adult    suspected 50-59 previous_day_admission_adult_covid_sus… 5.37e5 49367
## 14 adult    suspected 60-69 previous_day_admission_adult_covid_sus… 7.38e5 49367
## 15 adult    suspected 70-79 previous_day_admission_adult_covid_sus… 7.19e5 49367
## 16 adult    suspected 80+   previous_day_admission_adult_covid_sus… 6.55e5 49367
## 17 ped      confirmed 0-19  previous_day_admission_pediatric_covid… 1.67e5 49367
## 18 ped      suspected 0-19  previous_day_admission_pediatric_covid… 3.74e5 49367
saveToRDS(burdenPivotList_220907, ovrWriteError=FALSE)
saveToRDS(hospPerCap_220907, ovrWriteError=FALSE)

Peaks and valleys of key metrics are also updated:

peakValleyCDCDaily(cdc_daily_220907)
## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 6 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## Warning: Removed 20 row(s) containing missing values (geom_path).

## # A tibble: 7,740 × 8
##    date       state   vxa   vxc vxa_isPeak vxc_isPeak vxa_isValley vxc_isValley
##    <date>     <chr> <dbl> <dbl> <lgl>      <lgl>      <lgl>        <lgl>       
##  1 2020-12-01 CA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  2 2020-12-01 FL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  3 2020-12-01 GA       NA    NA FALSE      FALSE      FALSE        FALSE       
##  4 2020-12-01 IL       NA    NA FALSE      FALSE      FALSE        FALSE       
##  5 2020-12-01 MI       NA    NA FALSE      FALSE      FALSE        FALSE       
##  6 2020-12-01 NC       NA    NA FALSE      FALSE      FALSE        FALSE       
##  7 2020-12-01 NJ       NA    NA FALSE      FALSE      FALSE        FALSE       
##  8 2020-12-01 NY       NA    NA FALSE      FALSE      FALSE        FALSE       
##  9 2020-12-01 OH       NA    NA FALSE      FALSE      FALSE        FALSE       
## 10 2020-12-01 PA       NA    NA FALSE      FALSE      FALSE        FALSE       
## # … with 7,730 more rows
## # ℹ Use `print(n = ...)` to see more rows

Hospital capacity is updated using a mix of old data (for 2021) and new data:

identical(names(indivHosp_20220907), names(readFromRDS("indivHosp_20220704")))
## [1] TRUE
modHospData <- bind_rows(filter(readFromRDS("indivHosp_20220704"), lubridate::year(collection_week)<2022), 
                         filter(indivHosp_20220907, lubridate::year(collection_week)>=2022), 
                         .id="src"
                         )
updated_modStateHosp_20220907 <- hospitalCapacityCDCDaily(modHospData, 
                                                          plotSub="Aug 2020 to Aug 2022\nOld data used pre-2022"
                                                          )

Data availability by source and time is assessed:

# Temporary function to aggregate data
tempCounter <- function(df) {
    df %>%
        select(hospital_pk, collection_week, all_of(names(hhsMapper))) %>%
        colRenamer(vecRename=hhsMapper) %>%
        pivot_longer(-c(hospital_pk, collection_week)) %>%
        filter(!is.na(value), value>0) %>%
        count(collection_week, name)
}

dfTemp <- bind_rows(tempCounter(indivHosp_20220907), tempCounter(readFromRDS("indivHosp_20220704")), .id="src")

dfTemp %>%
    select(collection_week, name) %>%
    unique() %>%
    bind_rows(., ., .id="src")  %>%
    full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
    mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
    mutate(n=ifelse(is.na(n), 0, n)) %>%
    ggplot(aes(x=collection_week, y=n)) +
    geom_line(aes(group=src, color=src)) + 
    facet_wrap(~name) + 
    labs(title="Number of hospitals in US reporting >0 on metric by week", x=NULL, y="# Hospitals Reporting > 0") + 
    scale_color_discrete("Data Source:")

dfTemp %>%
    select(collection_week, name) %>%
    unique() %>%
    bind_rows(., ., .id="src")  %>%
    full_join(dfTemp, by=c("src", "collection_week", "name")) %>%
    mutate(src=c("1"="SEP-2022", "2"="JUL-2022")[src]) %>%
    mutate(n=ifelse(is.na(n), 0, n)) %>% 
    group_by(collection_week, name) %>% 
    summarize(delta=sum(ifelse(src=="SEP-2022", n, 0)-ifelse(src!="SEP-2022", n, 0)), .groups="drop") %>%
    ggplot(aes(x=collection_week, y=delta)) +
    geom_line(aes(color=case_when(delta>=0 ~ "darkgreen", TRUE ~ "red"))) +
    geom_hline(yintercept=0, lty=2) +
    geom_vline(xintercept=c(as.Date("2021-08-20"), as.Date("2022-06-24")), lty=2) +
    scale_color_identity(NULL) +
    facet_wrap(~name) + 
    labs(title="Delta in Number of hospitals in US reporting >0 on metric by week", 
         subtitle="Trend break dashed lines at 2021-08-20 and 2022-06-24",
         x=NULL, 
         y="Delta in # Hospitals Reporting > 0"
         )